{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "stuck-balloon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.13.2 | packaged by conda-forge | (main, Feb 17 2025, 14:10:22) [GCC 13.3.0]\n",
      "2025.09.1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit import rdBase, Chem, DataStructs\n",
    "from rdkit.Chem import AllChem, Draw, Descriptors, PandasTools, rdMolDescriptors, QED\n",
    "from rdkit.Chem.SpacialScore import SPS\n",
    "\n",
    "sys.path.append(os.path.join(os.environ['CONDA_PREFIX'],'share','RDKit','Contrib'))\n",
    "from SA_Score import sascorer\n",
    "from NP_Score import npscorer\n",
    "\n",
    "import QEPPI as ppi\n",
    "from QEPPI import QEPPI_Calculator, get_qeppi_properties\n",
    "\n",
    "PandasTools.RenderImagesInAllDataFrames(True)\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "import math\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "print(sys.version)\n",
    "print(rdBase.rdkitVersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "renewable-newsletter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "455\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDNUMBER</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ADD_INFO</th>\n",
       "      <th>H_ACCEPTORS</th>\n",
       "      <th>H_DONORS</th>\n",
       "      <th>ROTATABLE_BONDS</th>\n",
       "      <th>LOGP</th>\n",
       "      <th>LOGS</th>\n",
       "      <th>TPSA</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>ID</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>SALTDATA</th>\n",
       "      <th>STEREOCHEM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-504/07224027</td>\n",
       "      <td>17-(1,5-dimethylhexyl)-10,13-dimethyl-2,3,4,7,...</td>\n",
       "      <td>http://www.specs.net/enter.php?specsid=AA-504/...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.7399998</td>\n",
       "      <td>-8.9700003</td>\n",
       "      <td>20.23</td>\n",
       "      <td>N05</td>\n",
       "      <td>AA-504/07224027</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-504/07226009</td>\n",
       "      <td>Beta-Sitosterol</td>\n",
       "      <td>http://www.specs.net/enter.php?specsid=AA-504/...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9.6499996</td>\n",
       "      <td>-9.9499998</td>\n",
       "      <td>20.23</td>\n",
       "      <td>N07</td>\n",
       "      <td>AA-504/07226009</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-504/07617049</td>\n",
       "      <td>4,7-bis(dimethylamino)-3,10,12,12a-tetrahydrox...</td>\n",
       "      <td>http://www.specs.net/enter.php?specsid=AA-504/...</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.079999998</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>164.63</td>\n",
       "      <td>N05</td>\n",
       "      <td>AA-504/07617049</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-504/20833001</td>\n",
       "      <td>Angelicin (Isopsoralen)</td>\n",
       "      <td>http://www.specs.net/enter.php?specsid=AA-504/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0799999</td>\n",
       "      <td>-2.3399999</td>\n",
       "      <td>39.439999</td>\n",
       "      <td>N07</td>\n",
       "      <td>AA-504/20833001</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA-504/20833002</td>\n",
       "      <td>Psoralen (Ficusin)</td>\n",
       "      <td>http://www.specs.net/enter.php?specsid=AA-504/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>39.439999</td>\n",
       "      <td>N07</td>\n",
       "      <td>AA-504/20833002</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IDNUMBER                                               NAME  \\\n",
       "0  AA-504/07224027  17-(1,5-dimethylhexyl)-10,13-dimethyl-2,3,4,7,...   \n",
       "1  AA-504/07226009                                    Beta-Sitosterol   \n",
       "2  AA-504/07617049  4,7-bis(dimethylamino)-3,10,12,12a-tetrahydrox...   \n",
       "3  AA-504/20833001                            Angelicin (Isopsoralen)   \n",
       "4  AA-504/20833002                                 Psoralen (Ficusin)   \n",
       "\n",
       "                                            ADD_INFO H_ACCEPTORS H_DONORS  \\\n",
       "0  http://www.specs.net/enter.php?specsid=AA-504/...           1        1   \n",
       "1  http://www.specs.net/enter.php?specsid=AA-504/...           1        1   \n",
       "2  http://www.specs.net/enter.php?specsid=AA-504/...           9        5   \n",
       "3  http://www.specs.net/enter.php?specsid=AA-504/...           1        0   \n",
       "4  http://www.specs.net/enter.php?specsid=AA-504/...           1        0   \n",
       "\n",
       "  ROTATABLE_BONDS          LOGP        LOGS       TPSA PRICE               ID  \\\n",
       "0               5     8.7399998  -8.9700003      20.23   N05  AA-504/07224027   \n",
       "1               6     9.6499996  -9.9499998      20.23   N07  AA-504/07226009   \n",
       "2               3  -0.079999998       -3.78     164.63   N05  AA-504/07617049   \n",
       "3               0     2.0799999  -2.3399999  39.439999   N07  AA-504/20833001   \n",
       "4               0          1.67       -1.99  39.439999   N07  AA-504/20833002   \n",
       "\n",
       "                                              ROMol SALTDATA STEREOCHEM  \n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a260>      NaN        NaN  \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a2d0>      NaN        NaN  \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a340>      NaN        NaN  \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a3b0>      NaN        NaN  \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a420>      NaN        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = PandasTools.LoadSDF('data/cmpounds.sdf')\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "verified-desperate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDNUMBER</th>\n",
       "      <th>ROMol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-504/07224027</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-504/07226009</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-504/07617049</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-504/20833001</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA-504/20833002</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IDNUMBER                                             ROMol\n",
       "0  AA-504/07224027  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a260>\n",
       "1  AA-504/07226009  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a2d0>\n",
       "2  AA-504/07617049  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a340>\n",
       "3  AA-504/20833001  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a3b0>\n",
       "4  AA-504/20833002  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a420>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['IDNUMBER', 'ROMol']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fitting-korea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lipinski: Rule of five\n",
    "def rule_of_five(m):\n",
    "    mw = Descriptors.MolWt(m)\n",
    "    logp = Descriptors.MolLogP(m)\n",
    "    hbd = rdMolDescriptors.CalcNumLipinskiHBD(m)\n",
    "    hba = rdMolDescriptors.CalcNumLipinskiHBA(m)\n",
    "    psa = Descriptors.TPSA(m)\n",
    "    if (mw <= 500 and logp <= 5 and hbd <= 5 and hba <= 10):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sticky-winning",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rule of Four\n",
    "def rule_of_four(m):\n",
    "    mw = Descriptors.MolWt(m)\n",
    "    logp = Descriptors.MolLogP(m)\n",
    "    hba = rdMolDescriptors.CalcNumLipinskiHBA(m)\n",
    "    rings = AllChem.CalcNumRings(m)\n",
    "    if (mw >= 400 and logp >= 4 and rings >= 4 and hba >= 4):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "applicable-tamil",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rule of three\n",
    "def rule_of_three(m):\n",
    "    mw = Descriptors.MolWt(m)\n",
    "    logp = Descriptors.MolLogP(m)\n",
    "    hbd = rdMolDescriptors.CalcNumLipinskiHBD(m)\n",
    "    hba = rdMolDescriptors.CalcNumLipinskiHBA(m)\n",
    "    rotatable_bonds = Descriptors.NumRotatableBonds(m)\n",
    "    if (mw <= 300 and logp <= 3 and hbd <= 3 and hba <= 3 and rotatable_bonds <= 3):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "legendary-equilibrium",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ghose Filter\n",
    "def ghose_filter(m):\n",
    "    mw = Descriptors.MolWt(m)\n",
    "    logp = Descriptors.MolLogP(m)\n",
    "    NumAtoms= Chem.rdchem.Mol.GetNumAtoms(m)\n",
    "    mol_refractivity = Chem.Crippen.MolMR(m)\n",
    "    if (mw >= 160 and mw <= 480 and logp >= 0.4 and logp <= 5.6 and NumAtoms >= 20 and NumAtoms <= 70 and mol_refractivity >= 40 and mol_refractivity <= 130):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "unknown-alexander",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Veber Filter\n",
    "def veber_filter(m):\n",
    "    rotatable_bonds = Descriptors.NumRotatableBonds(m)\n",
    "    psa = Descriptors.TPSA(m)\n",
    "    if (rotatable_bonds <= 10 and psa <= 140):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unlimited-cholesterol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# REOS Filter\n",
    "def reos_filter(m):\n",
    "    mw = Descriptors.MolWt(m)\n",
    "    logp = Descriptors.MolLogP(m)\n",
    "    hba = rdMolDescriptors.CalcNumLipinskiHBA(m)\n",
    "    hbd = rdMolDescriptors.CalcNumLipinskiHBD(m)\n",
    "    rotatable_bonds = Descriptors.NumRotatableBonds(m)\n",
    "    formal_charge = Chem.rdmolops.GetFormalCharge(m)\n",
    "    NumHeavyAtom = Chem.rdchem.Mol.GetNumHeavyAtoms(m)\n",
    "    rings = AllChem.CalcNumRings(m)\n",
    "    if (mw >= 200 and mw <= 500 and logp >= int(0 - 5) and logp <= 5 and hbd >= 0 and hbd <= 5 and hba >= 0 and hba <= 10 and \n",
    "        formal_charge >= int(0-2) and formal_charge <= 2 and rotatable_bonds >= 0 and rotatable_bonds <= 8 and NumHeavyAtom >= 15 and NumHeavyAtom <= 50):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "racial-argument",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_65652/535821197.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Ro3'] = data.ROMol.map(rule_of_three)\n",
      "/tmp/ipykernel_65652/535821197.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Ro4'] = data.ROMol.map(rule_of_four)\n",
      "/tmp/ipykernel_65652/535821197.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Lipinski'] = data.ROMol.map(rule_of_five)\n",
      "/tmp/ipykernel_65652/535821197.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Ghose Filter'] = data.ROMol.map(ghose_filter)\n",
      "/tmp/ipykernel_65652/535821197.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"Veber Filter\"] = data.ROMol.map(veber_filter)\n",
      "/tmp/ipykernel_65652/535821197.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"REOS Filter\"] = data.ROMol.map(reos_filter)\n",
      "/tmp/ipykernel_65652/535821197.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['QED'] = data.ROMol.map(QED.qed)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDNUMBER</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>Ro3</th>\n",
       "      <th>Ro4</th>\n",
       "      <th>Lipinski</th>\n",
       "      <th>Ghose Filter</th>\n",
       "      <th>Veber Filter</th>\n",
       "      <th>REOS Filter</th>\n",
       "      <th>QED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-504/07224027</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.488312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-504/07226009</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.436096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-504/07617049</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.337567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-504/20833001</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.506483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA-504/20833002</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.506483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IDNUMBER                                             ROMol  Ro3  \\\n",
       "0  AA-504/07224027  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a260>    0   \n",
       "1  AA-504/07226009  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a2d0>    0   \n",
       "2  AA-504/07617049  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a340>    0   \n",
       "3  AA-504/20833001  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a3b0>    1   \n",
       "4  AA-504/20833002  <rdkit.Chem.rdchem.Mol object at 0x7e18b882a420>    1   \n",
       "\n",
       "   Ro4  Lipinski  Ghose Filter  Veber Filter  REOS Filter       QED  \n",
       "0    0         0             0             1            0  0.488312  \n",
       "1    0         0             0             1            0  0.436096  \n",
       "2    0         1             0             0            1  0.337567  \n",
       "3    0         1             0             1            0  0.506483  \n",
       "4    0         1             0             1            0  0.506483  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Ro3'] = data.ROMol.map(rule_of_three)\n",
    "data['Ro4'] = data.ROMol.map(rule_of_four)\n",
    "data['Lipinski'] = data.ROMol.map(rule_of_five)\n",
    "data['Ghose Filter'] = data.ROMol.map(ghose_filter)\n",
    "data[\"Veber Filter\"] = data.ROMol.map(veber_filter)\n",
    "data[\"REOS Filter\"] = data.ROMol.map(reos_filter)\n",
    "data['QED'] = data.ROMol.map(QED.qed)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "banner-newsletter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Lipinski', ylabel='QED'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\") \n",
    "sns.violinplot(x='Lipinski', y='QED', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "chubby-garden",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ro3', ylabel='QED'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\") \n",
    "sns.violinplot(x='Ro3', y='QED', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "viral-excitement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ro4', ylabel='QED'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\") \n",
    "sns.violinplot(x='Ro4', y='QED', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "intensive-stockholm",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RDKit313",
   "language": "python",
   "name": "rdkit313"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
